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Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

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  • Qingguang Zhang

    (Electronic and Information Engineering, Southern University of Science and Technology, Shenzhen 518055, China
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Mubasher Ikram

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Kun Xu

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

The penetration of electric vehicles (EVs) in vehicle-to-grid (V2G) interaction can effectively assist the grid in achieving frequency regulation and peak load balancing. However, the customer perceives that participating in V2G services would result in the additional cycling of the battery and the accelerated aging of the EVs’ power battery, which has become a major obstacle to the widespread adoption of V2G services. Most existing methods require long-term cycling data and battery parameters to quantify battery aging, which is not suitable for the V2G scenario with large-scale and short-time intervals. Consequently, the real-time and accurate quantification of battery aging for optimization remains a challenge. This study proposes a charging scheduling method for EVs that can accurately and online quantify battery aging. Firstly, V2G scheduling is formulated as an optimization problem by defining an online sliding window to collect real-time vehicle information on the grid, enabling online optimization. Secondly, battery aging is more accurately quantified by proposing a novel amplitude-based rain-flow cycle counting (MRCC) method, which utilizes the charging information of the battery within a shorter time period. Lastly, an intelligent optimization algorithm is employed to optimize the charging and discharging power of EVs, aiming to minimize grid fluctuations and battery aging. The proposed method is validated using a V2G scenario with 50 EVs with randomly generated behaviors, and the results demonstrate that the proposed online scheduling method not only reduces the EFCC of the battery by 8.4%, but also achieves results close to global optimization.

Suggested Citation

  • Qingguang Zhang & Mubasher Ikram & Kun Xu, 2024. "Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging," Energies, MDPI, vol. 17(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1681-:d:1368614
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    References listed on IDEAS

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    1. Fernandes, Camila & Frías, Pablo & Latorre, Jesús M., 2012. "Impact of vehicle-to-grid on power system operation costs: The Spanish case study," Applied Energy, Elsevier, vol. 96(C), pages 194-202.
    2. Ons Sassi & Ammar Oulamara, 2017. "Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 519-535, January.
    3. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    4. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
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